scholarly journals Immediate Benefits Realized Following Implementation of Physician Order Entry at an Academic Medical Center

2002 ◽  
Vol 9 (5) ◽  
pp. 529-539 ◽  
Author(s):  
H. S. Mekhjian ◽  
R. R. Kumar ◽  
L. Kuehn ◽  
T. D. Bentley ◽  
P. Teater ◽  
...  
Keyword(s):  
Medical Center ◽  
Academic Medical Center ◽  
Order Entry ◽  
Academic Medical ◽  
Physician Order Entry

scholarly journals A Tool to Retrieve Alert Dwell Time from a Homegrown Computerized Physician Order Entry (CPOE) System of an Academic Medical Center: An Exploratory Analysis

2021 ◽  
Vol 11 (24) ◽  
pp. 12004
Author(s):  
Shuo-Chen Chien ◽  
Yen-Po Chin ◽  
Chang-Ho Yoon ◽  
Chun-You Chen ◽  
Chun-Kung Hsu ◽  
...  
Keyword(s):  
Dwell Time ◽  
Medical Center ◽  
Academic Medical Center ◽  
Exploratory Analysis ◽  
Computerized Physician Order Entry ◽  
Order Entry ◽  
Professional Groups ◽  
Academic Medical ◽  
Physician Order Entry ◽  
The Impact

Alert dwell time, defined as the time elapsed from the generation of an interruptive alert to its closure, has rarely been used to describe the time required by clinicians to respond to interruptive alerts. Our study aimed to develop a tool to retrieve alert dwell times from a homegrown CPOE (computerized physician order entry) system, and to conduct exploratory analysis on the impact of various alert characteristics on alert dwell time. Additionally, we compared this impact between various professional groups. With these aims, a dominant window detector was developed using the Golang programming language and was implemented to collect all alert dwell times from the homegrown CPOE system of a 726-bed, Taiwanese academic medical center from December 2019 to February 2021. Overall, 3,737,697 interruptive alerts were collected. Correlation analysis was performed for alerts corresponding to the 100 most frequent alert categories. Our results showed that there was a negative correlation (ρ = −0.244, p = 0.015) between the number of alerts and alert dwell times. Alert dwell times were strongly correlated between different professional groups (physician vs. nurse, ρ = 0.739, p < 0.001). A tool that retrieves alert dwell times can provide important insights to hospitals attempting to improve clinical workflows.

scholarly journals A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center: Comprehensive alert characteristic analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246597
Author(s):  
Shuo-Chen Chien ◽  
Yen-Po (Harvey) Chin ◽  
Chang Ho Yoon ◽  
Md. Mohaimenul Islam ◽  
Wen-Shan Jian ◽  
...  
Keyword(s):  
Medical Center ◽  
Academic Medical Center ◽  
Computerized Physician Order Entry ◽  
Order Entry ◽  
Characteristic Analysis ◽  
Hospital System ◽  
Hospital Administrators ◽  
Academic Medical ◽  
Novel Method ◽  
Physician Order Entry

Background The collection and analysis of alert logs are necessary for hospital administrators to understand the types and distribution of alert categories within the organization and reduce alert fatigue. However, this is not readily available in most homegrown Computerized Physician Order Entry (CPOE) systems. Objective To present a novel method that can collect alert information from a homegrown CPOE system (at an academic medical center in Taiwan) and conduct a comprehensive analysis of the number of alerts triggered and alert characteristics. Methods An alert log collector was developed using the Golang programming language and was implemented to collect all triggered interruptive alerts from a homegrown CPOE system of a 726-bed academic medical center from November 2017 to June 2018. Two physicians categorized the alerts from the log collector as either clinical or non-clinical (administrative). Results Overall, 1,625,341 interruptive alerts were collected and classified into 1,474 different categories based on message content. The sum of the top 20, 50, and 100 categories of most frequently triggered alerts accounted for approximately 80, 90 and 97 percent of the total triggered alerts, respectively. Among alerts from the 100 most frequently triggered categories, 1,266,818 (80.2%) were administrative and 312,593 (19.8%) were clinical alerts. Conclusion We have successfully developed an alert log collector that can serve as an extended function to retrieve alerts from a homegrown CPOE system. The insight generated from the present study could also potentially bring value to hospital system designers and hospital administrators when redesigning their CPOE system.

Analysis of dosing-button compliance

2019 ◽  
Vol 76 (21) ◽  
pp. 1770-1776
Author(s):  
Mary Frances Picone ◽  
James P New ◽  
Matthew Hunter Johnson ◽  
Nihal Nilesh Desai ◽  
Matthew Hebbard
Keyword(s):  
Medical Center ◽  
Academic Medical Center ◽  
Order Entry ◽  
Exclusion Criteria ◽  
Computerized Provider Order Entry ◽  
High Performing ◽  
Academic Medical ◽  
Secondary Study ◽  
High Level ◽  
Initial Medication

Abstract Purpose A project was undertaken at an academic medical center to assess use of available dosing buttons within the computerized provider-order-entry (CPOE) system in order to identify opportunities for optimization of medication builds. Methods A retrospective observational study was conducted to identify medication records within a CPOE system meeting prespecified inclusion and exclusion criteria. A report capturing all inpatient adult medication orders associated with the identified medication records over a 6-month period was generated. The primary endpoint was percent dosing-button compliance, calculated as the number of orders with doses consistent with existing dosing-button options divided by the total number of orders during the study period. Secondary study objectives included a comparison of high- and low-performing medication record samples and identification of potential reasons for lack of dosing-button use. Results A total of 2,506 CPOE medication records associated with a total of 694,877 medication orders entered during the study period were analyzed. Median percent dosing-button compliance was 99.92% (interquartile range, 83.33–100%). High-performing records (n = 1243) were more likely to be associated with anti-infective medications (p = 0.041) and medications not on formulary at the study institution (p < 0.001). Medications in the sample of poor-performing CPOE records (n = 614) were more likely to be agents delivered via the i.v. route (p < 0.001). There were 45 records for which poor dosing-button compliance was attributed to lack of a clinically reasonable dosing option. Conclusion A high level of dosing-button compliance was demonstrated despite the lack of routine revalidation of dosing buttons after initial medication builds. Some opportunity for optimization was identified during the project, which established a quality assurance method to facilitate future auditing of medication builds.

Accuracy of and prescriber perceptions related to documenting antibiotic indications during order entry at an academic medical center

2020 ◽  
Vol 77 (4) ◽  
pp. 282-287
Author(s):  
James Beardsley ◽  
Mark Vestal ◽  
Norbert Rosario ◽  
Kalyn Meosky ◽  
James Johnson ◽  
...  
Keyword(s):  
Medical Center ◽  
Academic Medical Center ◽  
Final Diagnosis ◽  
Antibiotic Prescribing ◽  
Order Entry ◽  
Empiric Antibiotics ◽  
Academic Medical ◽  
Progress Notes ◽  
Secondary Endpoints ◽  
A Minor

Abstract Purpose To assess the accuracy of antibiotic indication documentation provided during order entry and prescriber perceptions of the requirement to specify indications. Methods Patients who received 1 of 6 selected antibiotics from May 1 through June 30, 2017, were identified. Records of 30 randomly selected patients who received each study antibiotic were retrospectively reviewed. The primary endpoint was indication accuracy, defined as agreement of the indication entered during order entry with that documented in progress notes at the time of order entry. Secondary endpoints included correlation of entered indication and final diagnosis for empiric antibiotics. A brief survey was emailed to prescribers to assess the burden and perceptions of requiring an indication during order entry. Results Four thousand five hundred twenty-four patients received 1 or more doses of a study antibiotic. For the 180 patients selected for evaluation, 89.4% of indications were accurate. Indications for antibiotics ordered for prophylaxis were more likely to be inaccurate than those for empiric or definitive antibiotics (accuracy rates of 46%, 94%, and 92%, respectively, p &lt; 0.05). For empiric antibiotics, 78.5% of indications documented at order entry matched the final diagnosis. Two hundred fifty-four of 863 prescribers (29%) responded to the survey request. Most respondents felt that documenting the indication took no more than 20 seconds, was a “minor nuisance” or “occasionally burdensome,” and had no impact on their consideration of antibiotic appropriateness. Conclusion With the exception of prophylaxis, the indications documented during order entry were sufficiently accurate to assist antimicrobial stewardship efforts. Although indication documentation was perceived as a minor burden, surveyed prescribers indicated it had only a minimal beneficial effect on antibiotic prescribing.

scholarly journals Impact of computerized provider order entry (CPOE) on length of stay and mortality

2016 ◽  
Vol 24 (2) ◽  
pp. 303-309 ◽  
Author(s):  
Ann M Lyons ◽  
Katherine A Sward ◽  
Vikrant G Deshmukh ◽  
Marjorie A Pett ◽  
Gary W Donaldson ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Medical Center ◽  
Statistical Tests ◽  
Academic Medical Center ◽  
Temporal Trends ◽  
Order Entry ◽  
Computerized Provider Order Entry ◽  
Local Setting ◽  
Academic Medical ◽  
Surgical Units

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE). Materials and Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome. Results: After controlling for covariates, CPOE remained a significant statistical predictor of decreased LOS and mortality. LOS decreased by 0.90 days, P &lt; .0001. Mortality decrease varied by model: 1 death per 1000 admissions (pre = 0.006, post = 0.0005, P &lt; .001) or 3 deaths (pre = 0.008, post = 0.005, P &lt; .01). Mortality and LOS decreased in medical and surgical units but increased in intensive care units. Discussion: This study examined CPOE at multiple levels. Given the inability to randomize CPOE assignment, these results may only be applicable to the local setting. Temporal trends found in this study suggest that hospital-wide implementations may have impacted nursing staff and new residents. Differences in the results were noted at the patient care unit and room levels. These differences may partly explain the mixed results from previous studies. Conclusion: Controlling for confounders, CPOE implementation remained a statistically significant predictor of LOS and mortality at this site. Mortality appears to be a sensitive outcome indicator with regard to hospital-wide implementations and should be further studied.

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